Parallel modeling of the electric field distribution in the brain

The term "Brain Imaging" identi�es a set of techniques to analyze the structure and/or functional behavior of the brain in normal and/or pathological situations. These techniques are largely used in the study of brain activity. In addition to clinical usage, analysis of brain activity is gaining popularity in others recent �fields, i.e. Brain Computer Interfaces (BCI) and the study of cognitive processes. In this context, usage of classical solutions (e.g. f MRI, PET-CT) could be unfeasible, due to their low temporal resolution, high cost and limited portability. For these reasons alternative low cost techniques are object of research, typically based on simple recording hardware and on intensive data elaboration process. Typical examples are ElectroEncephaloGraphy (EEG) and Electrical Impedance Tomography (EIT), where electric potential at the patient's scalp is recorded by high impedance electrodes. In EEG potentials are directly generated from neuronal activity, while in EIT by the injection of small currents at the scalp. To retrieve meaningful insights on brain activity from measurements, EIT and EEG relies on detailed knowledge of the underlying electrical properties of the body. This is obtained from numerical models of the electric �field distribution therein. The inhomogeneous and anisotropic electric properties of human tissues make accurate modeling and simulation very challenging, leading to a tradeo�ff between physical accuracy and technical feasibility, which currently severely limits the capabilities of these techniques. Moreover elaboration of data recorded requires usage of regularization techniques computationally intensive, which influences the application with heavy temporal constraints (such as BCI). This work focuses on the parallel implementation of a work-flow for EEG and EIT data processing. The resulting software is accelerated using multi-core GPUs, in order to provide solution in reasonable times and address requirements of real-time BCI systems, without over-simplifying the complexity and accuracy of the head models.

Phenotypic characterization of the epithelial and myoepithelial components in canine and feline mammary tumours

In veterinary medicine, the ability to classify mammary tumours based on the molecular profile and also determine whether the immunophenotype of the regional lymph node and/or systemic metastases is equal to that of the primary tumor may be predictive on the estimation of the effectiveness of various cancer treatments that can be scheduled. Therefore, aims, developed as projects, of the past three years have been (1) to define the molecular phenotype of feline mammary carcinomas and their lymph node metastases according to a previous modified algorithm and to demonstrate the concordance or discordance of the molecular profile between the primary tumour and lymph node metastasis, (2) to analyze, in female dogs, the relationship between the primary mammary tumor and its lymph node metastasis based on immunohistochemical molecular characterization in order to develop the most specific prognostic-predictive models and targeted therapeutic options, and (3) to evaluate the molecular trend of cancer from its primary location to systemic metastases in three cats and two dogs with mammary tumors. The studies on mammary tumours, particularly in dogs, have drawn gradually increasing attention not exclusively to the epithelial component, but also to the myoepithelial cells. The lack of complete information on a valid panel of markers for the identification of these cells in the normal and neoplastic mammary gland and lack of investigation of immunohistochemical changes from an epithelial to a mesenchymal phenotype, was the aim of a parallel research. While investigating mammary tumours, it was noticed that only few studies had focused on the expression of CD117. Therefore, it was decided to further deepen the knowledge in order to characterize the immunohistochemical staining of CD117 in normal and neoplastic mammary tissue of the dog, and to correlate CD117 immunohistochemical results with mammary histotype, histological stage (invasiveness), Ki67 index and patient survival time.